Please use this identifier to cite or link to this item:
https://hdl.handle.net/20.500.11851/10372
Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Ünal, P. | - |
dc.contributor.author | Albayrak, O. | - |
dc.contributor.author | Kubatova, M. | - |
dc.contributor.author | Deveci, B.U. | - |
dc.contributor.author | Çırakman, E. | - |
dc.contributor.author | Koçal, C.I. | - |
dc.contributor.author | Özbayoğlu, A. Murat | - |
dc.date.accessioned | 2023-04-16T10:01:19Z | - |
dc.date.available | 2023-04-16T10:01:19Z | - |
dc.date.issued | 2022 | - |
dc.identifier.isbn | 9781665480451 | - |
dc.identifier.uri | https://doi.org/10.1109/BigData55660.2022.10020608 | - |
dc.identifier.uri | https://hdl.handle.net/20.500.11851/10372 | - |
dc.description | Ankura;et al.;Hitachi;KPMG Consulting Co., Ltd.;NTT Data Intellilink Corporation;Think in Data Initiative, Association Inc | en_US |
dc.description | 2022 IEEE International Conference on Big Data, Big Data 2022 -- 17 December 2022 through 20 December 2022 -- 186390 | en_US |
dc.description.abstract | In the contemporary rotating machinery, bearings are critical and indispensable parts. Early detection of rolling bearing defects carries crucial importance, because undetected defects on the rolling bearings may end in loss of time, resources, money and even lives. In parallel to the accelerated utilization of deep learning applications in the manufacturing industry, different studies have been conducted to determine and evaluate defects on the surfaces of rolling bearings. In this study, a new system, that contains a hardware platform and software components in order to detect surface defects of the metal rolling bearings has been developed. To detect defects, optic image data of the bearings were used, and then computer vision and artificial intelligence techniques were applied to them. In the system, TC-VISION, the source of big data is the platform designed and developed using the optical camera. The results of the applied CNN algorithms performed better than the targeted values with respect to several metrics. The F1 score obtained is close to 100%. The developed system is aimed to be enhanced further in order to develop a fully automated inspection and quality control system for metal rolling bearing systems appropriate for serial production in real industrial environments. © 2022 IEEE. | en_US |
dc.description.sponsorship | Türkiye Bilimsel ve Teknolojik Araştırma Kurumu, TÜBİTAK: 3201195 | en_US |
dc.description.sponsorship | ACKNOWLEDGMENT This study was conducted by TEKNOPAR and supported by TÜBİTAK (The Scientific and Technological Research Council of Turkey) TC-VISION project, with project number 3201195. | en_US |
dc.language.iso | en | en_US |
dc.publisher | Institute of Electrical and Electronics Engineers Inc. | en_US |
dc.relation.ispartof | Proceedings - 2022 IEEE International Conference on Big Data, Big Data 2022 | en_US |
dc.rights | info:eu-repo/semantics/closedAccess | en_US |
dc.subject | artificial intelligence | en_US |
dc.subject | big data | en_US |
dc.subject | computer vision | en_US |
dc.subject | defect detection | en_US |
dc.subject | image classification with localization | en_US |
dc.subject | rolling bearings | en_US |
dc.subject | Big data | en_US |
dc.subject | Computer vision | en_US |
dc.subject | Deep learning | en_US |
dc.subject | Inspection | en_US |
dc.subject | Roller bearings | en_US |
dc.subject | Surface defects | en_US |
dc.subject | Bearing defect | en_US |
dc.subject | Big data applications | en_US |
dc.subject | Defect detection | en_US |
dc.subject | Hardware platform | en_US |
dc.subject | Image classification with localization | en_US |
dc.subject | Images classification | en_US |
dc.subject | Localisation | en_US |
dc.subject | Loss of time | en_US |
dc.subject | Manufacturing industries | en_US |
dc.subject | Rolling bearings | en_US |
dc.subject | Image classification | en_US |
dc.title | A Big Data Application in Manufacturing Industry-Computer Vision To Detect Defects on Bearings | en_US |
dc.type | Conference Object | en_US |
dc.department | TOBB ETÜ | en_US |
dc.identifier.startpage | 6074 | en_US |
dc.identifier.endpage | 6083 | en_US |
dc.identifier.scopus | 2-s2.0-85147911227 | en_US |
dc.institutionauthor | … | - |
dc.identifier.doi | 10.1109/BigData55660.2022.10020608 | - |
dc.authorscopusid | 56396952700 | - |
dc.authorscopusid | 57226393431 | - |
dc.authorscopusid | 58101098800 | - |
dc.authorscopusid | 57350944900 | - |
dc.authorscopusid | 58101982000 | - |
dc.authorscopusid | 58101633500 | - |
dc.authorscopusid | 57276609200 | - |
dc.relation.publicationcategory | Konferans Öğesi - Uluslararası - Kurum Öğretim Elemanı | en_US |
item.openairetype | Conference Object | - |
item.languageiso639-1 | en | - |
item.grantfulltext | none | - |
item.fulltext | No Fulltext | - |
item.openairecristype | http://purl.org/coar/resource_type/c_18cf | - |
item.cerifentitytype | Publications | - |
crisitem.author.dept | 02.1. Department of Artificial Intelligence Engineering | - |
Appears in Collections: | Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection |
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